Instructions to use crocutacrocuto/dinov2-large-MEG7-20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use crocutacrocuto/dinov2-large-MEG7-20 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="crocutacrocuto/dinov2-large-MEG7-20") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("crocutacrocuto/dinov2-large-MEG7-20") model = AutoModelForImageClassification.from_pretrained("crocutacrocuto/dinov2-large-MEG7-20") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 598f108822d912831d06aacd8660694c2f982ee0f5a36f009c5247fd88c646be
- Size of remote file:
- 5.37 kB
- SHA256:
- 5457a8bdb6fe1d90e7fb7b58eaa667a6745c9272028c1fc03ffd7ab269cc3fe0
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